Manager, Data Engineering

About Airwallex

Airwallex is the only unified payments and financial platform for global businesses. Powered by our unique combination of proprietary infrastructure and software, we empower over 250,000 businesses worldwide – including Brex, Navan, Qantas, SHEIN and many more – with fully integrated solutions to manage everything from business accounts, payments, spend management and treasury, to embedded finance at a global scale.

Proudly founded in Melbourne, we have a team of over 2,300 of the brightest and most innovative people in tech across 27 offices around the globe. Valued at US$11 billion and backed by world-leading investors including T. Rowe Price, Visa, Mastercard, Robinhood Ventures, Sequoia, Salesforce Ventures, DST Global, and Lone Pine Capital, Airwallex is leading the charge in building the global payments and financial platform of the future. If you’re ready to do the most ambitious work of your career, join us.

Attributes We Value

We hire successful builders with founder-like energy who want real impact, accelerated learning, and true ownership. You bring strong role-related expertise and sharp thinking, and you’re motivated by our mission and operating principles . You move fast with good judgment, dig deep with curiosity, and make decisions from first principles, balancing speed and rigor.

You're humble and collaborative; turn zero‑to‑one ideas into real products, and you “get stuff done” end-to-end. You use AI to work smarter and solve problems faster. Here, you’ll tackle complex, high‑visibility problems with exceptional teammates and grow your career as we build the future of global banking. If that sounds like you, let’s build what’s next.

About the team

The Data & AI org is at the heart of our company's data and AI strategy. We are building the foundational infrastructure that empowers the entire company to leverage data, AI, and ML into business impact. We accomplish this by creating platforms that handle the entire data and AI/ML lifecycle, simplifying the interface while providing proper safety and governance. This includes managing our data infrastructure (Databricks, Spark, Kafka, etc.), the technology to serve that data to our users (RAG, MCP, etc.), and the platform to host and govern these AI/ML models.

In 2026, our team’s overarching mission is to evolve our full data ecosystem—encompassing both platform and models—into a fully AI agent-ready infrastructure; we will empower customers to engage directly with the data platform to extract actionable value through capabilities like analytics and natural language querying, while also upgrading the platform to deliver robust, real-time performance for instant, data-driven decision-making.

What you'll do

We’re looking for a Data Engineering Manager to lead a team within our Strategic Data Org and help scale the data foundations that power Airwallex’s products, analytics, and operational decision-making. In this role, you will lead engineers working on data modeling, pipelines, and analytics-ready datasets across domains such as regulatory reporting, data content foundation, customer and business data, and growth data. You’ll partner closely with engineering leaders, product and business stakeholders, and adjacent platform teams to turn ambiguous business needs into reliable, well-structured data solutions.

This is a hybrid role based in San Francisco.

Team Leadership & People Management

Hire, coach, and grow a team of data engineers, setting clear expectations and providing regular feedback and career development support.

Establish team rituals, priorities, and ways of working that balance delivery speed with engineering rigor.

Act as a technical mentor, reviewing designs and code where needed, and helping engineers grow their skills in data modeling, pipeline engineering, and governance.

Manage performance, workload, and hiring plans in line with business needs.

Drive AI strategy and AI automation for the team.

Data Modeling Strategy

Set the technical direction for data modeling across the team, ensuring the org selects appropriate schema designs (e.g., star schema, snowflake, normalized vs. denormalized) based on business use cases.

Champion the concept of Single Source of Truth (SSOT) across data layers and pipelines, and hold the team accountable to it.

Ensure your team collaborates effectively with business stakeholders to translate data needs into clean, structured, well-documented models.

Oversee data consistency, traceability, and quality standards across multiple data sources and domains.

ETL & Data Pipeline Oversight